Simplifying protein engineering with deep learning.
Cell, 188(17): 4477-4479 PMID 40845808 DOI 10.1016/j.cell.2025.07.037
Аннотация
When it comes to deep learning for protein engineering, there is strength in simplicity. In this issue of Cell, through thoughtful deployment of existing fixed-backbone sequence design models, Caixia Gao and colleagues engineer diverse genome editing systems with improved functionality, enabling powerful capabilities in fine-grained and large-scale genome editing as demonstrated through strong experimental validation.